The code of HSEmotionONNX Python Library is released under the Apache-2.0 License. There is no limitation for both academic and commercial usage.
python setup.py install
It is also possible to install it via pip:
pip install hsemotion-onnx
from hsemotion_onnx.facial_emotions import HSEmotionRecognizer
model_name='enet_b0_8_best_afew'
fer=HSEmotionRecognizer(model_name=model_name)
emotion,scores=fer.predict_emotions(face_img,logits=False)
The following values of model_name parameter are supported:
- enet_b0_8_best_vgaf
- enet_b0_8_best_afew
- enet_b0_8_va_mtl
- enet_b2_8
- enet_b2_7
The method predict_emotions returns both the string value of predicted emotions (Anger, Contempt, Disgust, Fear, Happiness, Neutral, Sadness, or Surprise) and scores at the output of the last layer.
If the logits parameter is set to True (by default), the logits are returned, otherwise, the posterior probabilities are estimated from the logits using softmax.
The versions of this method for a batch of images are also available
emotions,scores=fer.predict_multi_emotions(face_img_list,logits=False)
Complete usage examples are available in the demo folder. It is necessary to install mediapipe to run the demo script.
The details about training of the models are available in the main repository